Route maps, when well designed, are an effective device for visualizing and communicating directions. Such maps have existed in various forms for centuries, and the recent availability of detailed geographic databases via the Internet has led to the widespread use of computer-generated route maps. Online mapping services typically provide directions as a set of maps complemented with text descriptions. Such on-line computer-generated maps are unsatisfactory, however, because the algorithms used to generate the maps disregard many of the techniques and principles used by human map-makers.
Effective use of a route map generally requires two distinct activities: (i) following a path until reaching a critical point and (ii) changing orientation at that point to follow another path. Thus, one of the most important types of information route maps can communicate are points of reorientation, that is, point along the route where someone must consciously turn from one path to another. However, existing computer-generated route maps fail to effectively communicate points of reorientation because they scale all the roads in the map by a constant scale factor. The scaling of all the roads in a route map by a constant scale factor is referred to herein as uniform scaling. As a result of uniform scaling, for routes of any reasonable length, uniform scaling frequently requires some roads to be very short. But it is often precisely these very short roads that connect critical turning points. Thus, uniform scaling can result in a loss of some of the most critical information found in a route map.
Another shortcoming in prior art computer-generated route maps is that they needlessly depict accurate length, angle, and curvature of each road in the route. Such accurate depictions are made at the expense of map readability. Psychological research indicates that most people distort distances, angles, and curvature when drawing route maps. See e.g., Tversky and Lee, “How space structures language,” Spatial Cognition: An interdisciplinary approach to representation and processing of spatial knowledge, (eds.) Freska, Habel, and Wender, 1998, 157-175; Tversky and Lee, “Pictorial and Verbal Tools for Conveying Routes,” COSIT 99, Conference Proceedings, Stade Germany, 1999, 51-64. Other psychological studies indicate that people maintain such distortions in their own mental representations of a route. See e.g., Tversky, “Distortions in Cognitive Maps,” Geoforum 23, 1992, 131-138. Thus, adherence to accurate lengths and angles in prior art computer-generated maps runs counter to how humans conceptualize routes.
Computer-generated route maps can be classified into four major mapping styles: route highlight maps, TripTiks, overview/detail maps, and two dimensional nonlinear distortion maps. Route highlight maps simply highlight the route on a general road map of the region, as shown in FIG. 1. Since the purpose of general road maps is to provide an understanding of the entire road system in a region, such maps typically employ constant scale factors and display extraneous detail throughout the map. The constant scaling, as exhibited in FIG. 1, generally causes one of two problems. Either detailed turn information is lost because the scale factor is too large, or the scale factor is small enough to show the detail, but the map is very large. Since general road maps are not optimized to show any particular route, a route highlight map will often suffer from both a large scale factor and an inconvenient size. The clarity of the route in a route highlight map depends on the style of the highlighting since that is the only property differentiating the route from other roads. Usually the route is distinctively colored, but because general road maps provide context information over the entire map, the map is cluttered with extraneous information that makes it difficult to perceive the route and the individual reorientation points.
TripTiks are similar to route highlight maps, but they are specifically designed for communicating a particular route. As shown in FIG. 2, a TripTik map usually stretches over multiple rectangular pages, and each page is oriented so that the route runs roughly down the center of the page. Each TripTik page employs constant scaling, but the scale factor differs across pages. Changing the scale factor from page to page allows the TripTik to show more detailed turn information where needed. However, because the map stretches over many pages and the orientation and scale factor varies from page to page, forming a general understanding of the overall route is difficult.
Overview/detail maps combine multiple maps rendered at different scales to present a single route, as shown in FIG. 3. One of the maps (e.g., FIG. 3A) is scaled by a large factor so that it provides an overview of the entire route. Since the large scale factor of this map reduces the readability of local turn details, maps showing turn-by-turn information are provided (e.g., FIG. 3B). A constant scale factor is used for each map, but the scale factor differs across the maps. While an overview/detail map may seem like an effective combination, such maps are unsatisfactory in practice. The overview map rarely presents more than the overall direction and context of the route. Although turn-by-turn maps provide detailed information for every turn, the use of distinct maps for each turn, often with different orientation and scale, makes it difficult to understand how the maps correspond to one another. Therefore, the navigator has difficulty forming a cognitive model of the route.
To ensure clear communication of all of the reorientation points, some parts of a route's depiction may require a small scale factor while others require a large scale factor. Researchers have described attempts to use two dimensional nonlinear image distortion techniques on general road maps to provide focus-plus-context viewing. (See. e.g., Carpendale et al., “Three-Dimensional Pliable Surfaces: For the Effective Presentation of Visual Information,” Proceedings of the ACM Symposium on User Interface Software and Technology, UIST 95, 1995, 217-226; Keahey, “The Generalized Detail-In-Context Problem,” Proceedings of the IEEE Symposium on Information Visualization, IEEE Visualization 1998). These techniques allow users to choose regions of the map they want to focus on and then apply a nonlinear magnification, such as a spherical distortion, to enlarge these focus regions. Such two dimensional distortion allows detailed information to be displayed only where relevant and often produces general area maps that can be conveniently displayed on a single page. However, a major problem with nonlinear two-dimensional distortion is that the regions at the edges between the magnified and non-magnified portions of the map undergo extreme distortion.
In an effective route map, all essential components of the route, especially the roads, are easily identifiable. The route is clearly marked and readily apparent even at a quick glance. The map contains only as much information as is necessary and is easy to carry and manipulate. To further such design goals, map content, precision, and rendering style must be carefully optimized. Map content includes important parameters such as a route start and end, as well as points of reorientation. Although all maps are abstract representations of a route, there is a range of styles that can be used to render a map, with varying associations of accuracy and realism. An appropriate rendering style can greatly affect the readability and clarity of a map. Retinal properties such as color and line thickness are used to draw attention to important features of the map. Rendering style can also aid the user in interpreting how closely the map corresponds with the real world. Another important map design goal is the proper use of context information. The amount of context information included in the map greatly affects the utility of the map. Useful context information includes labels or names for a path on the route as well as context information along the route such as buildings, stop lights, or stop signs. When drawing a route map by hand, people most commonly use context information to indicate points of reorientation and, less frequently, to communicate progress along a road.
Environmental psychology studies have demonstrated that human generated route maps contain distortion. There are three primary types of distortion: (1) inaccurate path lengths, (2) incorrect turning angles at intersections, and (3) simplified road shape. For example, Tversky and Lee, COSIT 99 Conference Proceedings, 1999, 51-64, asked a group of students to sketch a route map between two locations near the Stanford University campus. Although they encouraged participants in their study to represent paths and intersections accurately, most did not. Most intersections were drawn at right angles regardless of their actual angle and seventy-one percent of the participants used simple generic curves and straight lines to represent roads. Even when participants intended to communicate the shape or length of the road accurately, they typically rendered these attributes incorrectly. Such distortion in the map is in fact beneficial because it increases the flexibility available to the map-maker in the design and layout of the map. Variably scaling the length of each road allows the map-maker to ensure all reorientation points are visible, while flexibility in choosing turning angles and road curvature allows the map to be simplified. Such distortions can simultaneously improve the readability and convenience of the route map with little adverse effect on its clarity and completeness.
Hand-drawn route maps often present a good combination of readability, clarity, completeness and convenience, as shown in FIG. 4. Instead of using a constant scale factor, hand-drawn maps only maintain the relative ordering of roads by length. While this ensures that longer roads appear longer than shorter roads in the map, each road is scaled by a different factor. Often the map designer does not know the exact length of the roads and only knows their lengths relative to one another. The flexibility of relative scaling allows hand-drawn route maps to fit within a manageable size and remain readable.
Hand-drawn route maps typically remove most contextual information that does not lie directly along the route. This strategy reduces overall clutter and improves clarity. The intersection angles in hand-drawn maps are generally incorrect, the precise shape of roads is often misrepresented, and the roads are typically depicted as generically straight or curved. These distortions make the map simpler and only remove unnecessary information. Hand-drawn route maps are rendered in a “sketchy” style typical of quick pen-and-ink doodling. Many navigators are familiar with such hand-drawn maps and the sketchy style is a subtle indicator of imprecision in the map.
In order to improve route map clarity, many algorithms have been developed for smoothing, interpolating, and simplifying roads in a route map. In the area of map rendering the most well-known simplification algorithms are Douglas & Peucker, “Algorithms for the reduction of the number of points required to represent a digitized line or its caricature,” The Canadian Cartographer 10(2), 1973, 112-22; Ramer, “An iterative approach for polygonal approximation of planar closed curves,” Computer Graphics and Image Processing. 1, 1972, 244-56isvalingam & Whyatt, “Line generalization by repeated elimination of points,” Cartographic Journal. 30(1), 1993, 46-51; and Barkowsky, Latecki, and Richter, “Schematizing maps: Simplification of geographic shape by discrete curve evolution,” in Freksa, Brauer, Habel, and Wender (eds.): Spatial Cognition II, Springer-Verlag, Berlin, in press. Given a piecewise linear curve as a set of shape points, all of these methods remove some subset of the shape points to produce a simpler curve. Examples of shape points 3302 and turning points 3306 are provided in FIG. 33A. Each of these methods uses different criteria/metrics to decide which shape points to remove and which to retain. As roads become simpler both the perceptual benefits and processing speed increase. The most extreme form of simplification replaces the piecewise linear road with a single linear segment from the first shape point to the last shape point. Although this extreme approach produces a good approximation in most cases, it can cause the map to become misleading. Prior art algorithms for simplifying roads in a route map can generate three types of undesirable results:
(i) False Intersections. Roads that did not intersect before simplification falsely intersect after simplification. An example of a false intersection 3310 is found in FIG. 33A.
(ii) Missing Intersections. Roads that did intersect before simplification no longer intersect after simplification. An example of a missing intersection 3312 is found in FIG. 33B.
(iii) Inconsistent Turning Angles. The turning angle between roads can change substantially, even to the point where a left turn might appear as a right turn. An example of a wrong turn angle 3314 is found in FIG. 33C.
Based on the above background it is apparent that what is needed in the art is an improved system and method for making computer-generated maps. What is further needed in the art is a system and method for making computer generated maps that avoid the pitfalls found in existing map-making algorithms, such as the use of extraneous information and constant scaling.